Techniques for cascade filtering of points of interest in point clouds

ABSTRACT

Techniques for cascade filtering of a set of points of interest (POIs) in a light detection and ranging (LiDAR) system is described. The method includes performing a series of cascaded filtering of a set of points of interest (POIs) in a first point cloud and a second point cloud of the LiDAR system. The method also includes calculating at least a first metric over at least the POI and to make a decision with respect to the second point cloud, and extracting at least one of range and velocity information based on the second point cloud.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/342,247 filed on Jun. 8, 2021, which claims priority from and thebenefit of U.S. Provisional Patent Application No. 63/092,228 filed onOct. 15, 2020, the entire contents of which are incorporated herein byreference in their entirety.

TECHNICAL FIELD

The present disclosure relates generally to point set or point cloudfiltering techniques and, more particularly, point set or point cloudfiltering techniques for use in a light detection and ranging (LiDAR)system.

BACKGROUND

Frequency-Modulated Continuous-Wave (FMCW) LiDAR systems include severalpossible phase impairments such as laser phase noise, circuitry phasenoise, flicker noise that the driving electronics inject on a laser,drift over temperature/weather, and chirp rate offsets. FMCW LiDAR pointclouds may exhibit distinct noise patterns, which may arise fromincorrect peak matching leading to falsely detected points that appearin the scene even when nothing is present. For example, when an FMCWLiDAR points to a fence or a bush, a number of ghost points may appearin the scene between the LiDAR and the fence. These ghost points ornoisy points, which are also classified as False Alarm (FA) points, ifleft unfiltered, may introduce ghost objects and cause errors in theestimated target range/velocity.

SUMMARY

The present disclosure describes various examples of point cloudfilters, e.g., series cascaded filters in LiDAR systems.

In some examples, disclosed herein is a method of filtering a pointcloud. The characteristic features of FA points, which distinguish theFA points from true detections, may be exploited to identify the FApoints and regions. When a point cloud is passed to a filteringalgorithm, referred to herein as a filter, the filter works on either asingle point or multiple points, referred to as points of interest(POI), at a given time. Some points and statistics from the neighborhoodof the POI may be provided to the filter to provide a context. Thecontext may be used to make a decision on the POI to check if thecharacteristics of the POI are consistent with the neighborhood points.The context may include contextual data around the POI to aid the filterto make a decision on the POI, by checking POI's consistency with theneighborhood points. Different metrics may be formulated to quantifythese statistics/characteristics. Multiple filters may be designed toidentify FA points with characteristics that are different from thepoint cloud. The identified FA points are then subsequently modified orremoved from the point cloud. The resulting point cloud is a filteredout version of the original point cloud without the FA points. Forexample, the filter may iteratively get a POI from the point cloud,select points in the neighborhood of the POI to provide a context to thefilter, and calculate a metric over the POI and its neighbors and thenmake a decision to keep, remove, or modify the POI (e.g., based on thecalculated metric).

In some examples, a method of filtering points in a point cloud isdisclosed herein. A set of POIs of a first point cloud are received,where each POI of the set of POIs comprises one or more points. Each POIof the set of POIs is filtered, where filtering comprises filtering at afirst filter and filtering at a second filter. At the first filter, afirst set of neighborhood points of a POI is selected; a first metricfor the first set of neighborhood points is computed; and based on thefirst metric, whether to accept the POI, modify the POI, reject the POI,or transmit the POI to a second filter is determined. Provided the POIis accepted or modified at the first filter, the POI is transmitted to asecond point cloud; provided the POI is rejected at the first filter,the POI is prevented from reaching the second point cloud; provided thePOI is not accepted, modified, or rejected at the first filter, the POIis transmitted to a second filter to determine whether to accept,modify, or reject the POI. At the second filter, provided the POI isaccepted or modified, the POI is transmitted to the second point cloud.At least one of range and velocity information is extracted based on thesecond point cloud.

In some examples, a LiDAR system is disclosed herein. The LiDAR systemcomprises a memory and a processing device, operatively coupled with thememory. The processing device is to receive a set of POIs of a firstpoint cloud, wherein each POI of the set of POIs comprises one or morepoints. The processing device is to filter each POI of the set of POIsat a first filter and at a second filter. At the first filter, theprocessing device is to select a first set of neighborhood points of aPOI; compute a first metric for the first set of neighborhood points;and determine, based on the first metric, whether to accept the POI,modify the POI, reject the POI, or transmit the POI to a second filter.Provided the POI is accepted or modified at the first filter, theprocessing device is to transmit the POI to a second point cloud;provided the POI is rejected at the first filter, the processing deviceis to prevent the POI from reaching the second point cloud; provided thePOI is not accepted, modified, or rejected at the first filter, theprocessing device is to transmit the POI to a second filter to determinewhether to accept, modify, or reject the POI. At the second filter,provided the POI is accepted or modified, the processing device is totransmit the POI to the second point cloud. The processing device is toextract at least one of range and velocity information based on thesecond point cloud.

In some examples, a non-transitory machine-readable medium is disclosedherein. The non-transitory machine-readable medium has instructionsstored therein, which when executed by a processing device, cause theprocessing device to receive a set of POIs of a first point cloud,wherein each POI of the set of POIs comprises one or more points. Theprocessing device is to filter each POI of the set of POIs at a firstfilter and at a second filter. At the first filter, the processingdevice is to select a first set of neighborhood points of a POI; computea first metric for the first set of neighborhood points; and determine,based on the first metric, whether to accept the POI, modify the POI,reject the POI, or transmit the POI to a second filter. Provided the POIis accepted or modified at the first filter, the processing device is totransmit the POI to a second point cloud; provided the POI is rejectedat the first filter, the processing device is to prevent the POI fromreaching the second point cloud; provided the POI is not accepted,modified, or rejected at the first filter, the processing device is totransmit the POI to a second filter to determine whether to accept,modify, or reject the POI. At the second filter, provided the POI isaccepted or modified, the processing device is to transmit the POI tothe second point cloud. The processing device is to extract at least oneof range and velocity information based on the second point cloud. Itshould be appreciated that, although one or more embodiments in thepresent disclosure depict the use of point clouds, embodiments of thepresent invention are not limited as such and may include, but are notlimited to, the use of point sets and the like.

These and other aspects of the present disclosure will be apparent froma reading of the following detailed description together with theaccompanying figures, which are briefly described below. The presentdisclosure includes any combination of two, three, four or more featuresor elements set forth in this disclosure, regardless of whether suchfeatures or elements are expressly combined or otherwise recited in aspecific example implementation described herein. This disclosure isintended to be read holistically such that any separable features orelements of the disclosure, in any of its aspects and examples, shouldbe viewed as combinable unless the context of the disclosure clearlydictates otherwise.

It will therefore be appreciated that this Summary is provided merelyfor purposes of summarizing some examples so as to provide a basicunderstanding of some aspects of the disclosure without limiting ornarrowing the scope or spirit of the disclosure in any way. Otherexamples, aspects, and advantages will become apparent from thefollowing detailed description taken in conjunction with theaccompanying figures which illustrate the principles of the describedexamples.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of various examples, reference is nowmade to the following detailed description taken in connection with theaccompanying drawings in which like identifiers correspond to likeelements:

FIG. 1A is a block diagram illustrating an example LiDAR systemaccording to embodiments of the present disclosure.

FIG. 1B is a block diagram illustrating an example of a point cloudfiltering module of a LiDAR system according to embodiments of thepresent disclosure.

FIG. 2 is a time-frequency diagram illustrating an example of FMCW LIDARwaveforms according to embodiments of the present disclosure.

FIG. 3A is a block diagram illustrating an example of a point cloudfilter according to embodiments of the present disclosure.

FIG. 3B is a block diagram illustrating an example of a filter core of apoint cloud filter according to embodiments of the present disclosure.

FIG. 4A is a block diagram illustrating an example of a simple filtercore according to embodiments of the present disclosure.

FIG. 4B is a block diagram illustrating another example of a simplefilter core according to embodiments of the present disclosure.

FIG. 5A is a block diagram illustrating an example of a complex filtercore, according to embodiments of the present disclosure.

FIG. 5B is a block diagram illustrating another example of a complexfilter core, according to embodiments of the present disclosure.

FIG. 6 is a block diagram illustrating an example of a series cascadedpoint cloud filter according to embodiments of the present disclosure.

FIG. 7 is a block diagram illustrating detailed structure of a seriescascaded point cloud filter according to embodiments of the presentdisclosure.

FIG. 8 is a block diagram illustrating an example of multiple seriescascaded point cloud filters according to embodiments of the presentdisclosure.

FIG. 9 is a block diagram illustrating an example of a process to filtera point cloud according to embodiments of the present disclosure.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosures will be describedwith reference to details discussed below, and the accompanying drawingswill illustrate the various embodiments. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosures.

The described LiDAR systems herein may be implemented in any sensingmarket, such as, but not limited to, transportation, manufacturing,metrology, medical, virtual reality, augmented reality, and securitysystems. According to some embodiments, the described LiDAR system maybe implemented as part of a front-end of frequency modulatedcontinuous-wave (FMCW) device that assists with spatial awareness forautomated driver assist systems, or self-driving vehicles.

FIG. 1A illustrates a LiDAR system 100 according to exampleimplementations of the present disclosure. The LiDAR system 100 includesone or more of each of a number of components, but may include fewer oradditional components than shown in FIG. 1. According to someembodiments, one or more of the components described herein with respectto LiDAR system 100 can be implemented on a photonics chip. The opticalcircuits 101 may include a combination of active optical components andpassive optical components. Active optical components may generate,amplify, and/or detect optical signals and the like. In some examples,the active optical component includes optical beams at differentwavelengths, and includes one or more optical amplifiers, one or moreoptical detectors, or the like.

Free space optics 115 may include one or more optical waveguides tocarry optical signals, and route and manipulate optical signals toappropriate input/output ports of the active optical circuit. The freespace optics 115 may also include one or more optical components such astaps, wavelength division multiplexers (WDM), splitters/combiners,polarization beam splitters (PBS), collimators, couplers or the like. Insome examples, the free space optics 115 may include components totransform the polarization state and direct received polarized light tooptical detectors using a PBS, for example. The free space optics 115may further include a diffractive element to deflect optical beamshaving different frequencies at different angles.

In some examples, the LiDAR system 100 includes an optical scanner 102that includes one or more scanning mirrors that are rotatable along anaxis (e.g., a slow-moving-axis) that is orthogonal or substantiallyorthogonal to the fast-moving-axis of the diffractive element to steeroptical signals to scan a target environment according to a scanningpattern. For instance, the scanning mirrors may be rotatable by one ormore galvanometers. Objects in the target environment may scatter anincident light into a return optical beam or a target return signal. Theoptical scanner 102 also collects the return optical beam or the targetreturn signal, which may be returned to the passive optical circuitcomponent of the optical circuits 101. For example, the return opticalbeam may be directed to an optical detector by a polarization beamsplitter. In addition to the mirrors and galvanometers, the opticalscanner 102 may include components such as a quarter-wave plate, lens,anti-reflective coating window or the like.

To control and support the optical circuits 101 and optical scanner 102,the LiDAR system 100 includes LiDAR control systems 110. The LiDARcontrol systems 110 may include a processing device for the LiDAR system100. In some examples, the processing device may be one or moregeneral-purpose processing devices such as a microprocessor, centralprocessing unit, or the like. More particularly, the processing devicemay be complex instruction set computing (CISC) microprocessor, reducedinstruction set computer (RISC) microprocessor, very long instructionword (VLIW) microprocessor, or processor implementing other instructionsets, or processors implementing a combination of instruction sets. Theprocessing device may also be one or more special-purpose processingdevices such as an application specific integrated circuit (ASIC), afield programmable gate array (FPGA), a digital signal processor (DSP),network processor, or the like.

In some examples, the LiDAR control systems 110 may include a signalprocessing unit 112 such as a digital signal processor (DSP). The LiDARcontrol systems 110 are configured to output digital control signals tocontrol optical drivers 103. In some examples, the digital controlsignals may be converted to analog signals through signal conversionunit 106. For example, the signal conversion unit 106 may include adigital-to-analog converter. The optical drivers 103 may then providedrive signals to active optical components of optical circuits 101 todrive optical sources such as lasers and amplifiers. In some examples,several optical drivers 103 and signal conversion units 106 may beprovided to drive multiple optical sources.

The LiDAR control systems 110 are also configured to output digitalcontrol signals for the optical scanner 102. A motion control system 105may control the galvanometers of the optical scanner 102 based oncontrol signals received from the LIDAR control systems 110. Forexample, a digital-to-analog converter may convert coordinate routinginformation from the LiDAR control systems 110 to signals interpretableby the galvanometers in the optical scanner 102. In some examples, amotion control system 105 may also return information to the LiDARcontrol systems 110 about the position or operation of components of theoptical scanner 102. For example, an analog-to-digital converter may inturn convert information about the galvanometers' position to a signalinterpretable by the LIDAR control systems 110.

The LiDAR control systems 110 are further configured to analyze incomingdigital signals. In this regard, the LiDAR system 100 includes opticalreceivers 104 to measure one or more beams received by optical circuits101. For example, a reference beam receiver may measure the amplitude ofa reference beam from the active optical component, and ananalog-to-digital converter converts signals from the reference receiverto signals interpretable by the LiDAR control systems 110. Targetreceivers measure the optical signal that carries information about therange and velocity of a target in the form of a beat frequency,modulated optical signal. The reflected beam may be mixed with a secondsignal from a local oscillator. The optical receivers 104 may include ahigh-speed analog-to-digital converter to convert signals from thetarget receiver to signals interpretable by the LiDAR control systems110. In some examples, the signals from the optical receivers 104 may besubject to signal conditioning by signal conditioning unit 107 prior toreceipt by the LiDAR control systems 110. For example, the signals fromthe optical receivers 104 may be provided to an operational amplifierfor amplification of the received signals and the amplified signals maybe provided to the LIDAR control systems 110.

In some applications, the LiDAR system 100 may additionally include oneor more imaging devices 108 configured to capture images of theenvironment, a global positioning system 109 configured to provide ageographic location of the system, or other sensor inputs. The LiDARsystem 100 may also include an image processing system 114. The imageprocessing system 114 can be configured to receive the images andgeographic location, and send the images and location or informationrelated thereto to the LiDAR control systems 110 or other systemsconnected to the LIDAR system 100.

In operation according to some examples, the LiDAR system 100 isconfigured to use nondegenerate optical sources to simultaneouslymeasure range and velocity across two dimensions. This capability allowsfor real-time, long range measurements of range, velocity, azimuth, andelevation of the surrounding environment.

In some examples, the scanning process begins with the optical drivers103 and LiDAR control systems 110. The LiDAR control systems 110instruct the optical drivers 103 to independently modulate one or moreoptical beams, and these modulated signals propagate through the passiveoptical circuit to the collimator. The collimator directs the light atthe optical scanning system that scans the environment over apreprogrammed pattern defined by the motion control system 105. Theoptical circuits 101 may also include a polarization wave plate (PWP) totransform the polarization of the light as it leaves the opticalcircuits 101. In some examples, the polarization wave plate may be aquarter-wave plate or a half-wave plate. A portion of the polarizedlight may also be reflected back to the optical circuits 101. Forexample, lensing or collimating systems used in LIDAR system 100 mayhave natural reflective properties or a reflective coating to reflect aportion of the light back to the optical circuits 101.

Optical signals reflected back from the environment pass through theoptical circuits 101 to the receivers. Because the polarization of thelight has been transformed, it may be reflected by a polarization beamsplitter along with the portion of polarized light that was reflectedback to the optical circuits 101. Accordingly, rather than returning tothe same fiber or waveguide as an optical source, the reflected light isreflected to separate optical receivers. These signals interfere withone another and generate a combined signal. Each beam signal thatreturns from the target produces a time-shifted waveform. The temporalphase difference between the two waveforms generates a beat frequencymeasured on the optical receivers (photodetectors). The combined signalcan then be reflected to the optical receivers 104.

The analog signals from the optical receivers 104 are converted todigital signals using ADCs. The digital signals are then sent to theLiDAR control systems 110. A signal processing unit 112 may then receivethe digital signals and interpret them. In some embodiments, the signalprocessing unit 112 also receives position data from the motion controlsystem 105 and galvanometers (not shown) as well as image data from theimage processing system 114. The signal processing unit 112 can thengenerate a 3D point cloud with information about range and velocity ofpoints in the environment as the optical scanner 102 scans additionalpoints. The signal processing unit 112 can also overlay a 3D point clouddata with the image data to determine velocity and distance of objectsin the surrounding area. The system also processes the satellite-basednavigation location data to provide a precise global location.

FIG. 1B is a block diagram 100 b illustrating an example of a pointcloud filtering module 140 in a LiDAR system according to embodiments ofthe present disclosure. The signal processing unit 112 may include thepoint cloud filtering module 140. It should be noted that, although thepoint cloud filtering module is depicted as residing within the signalprocessing unit 112, embodiments of the present disclosure are notlimited as such. For instance, in one embodiment, the point cloudfiltering module 140 can reside in computer memory (e.g., RAM, ROM,flash memory, and the like) within system 100 (e.g., Lidar controlsystem 110).

With reference to FIG. 1B, the point cloud filtering module 140 includesthe functionality to select neighborhood data points from a set ofpoints, compute metrics, and make determinations concerning theacceptance, modification, removal and/or transmission of points to apoint set or point cloud using one or more filters.

For instance, the point cloud filtering module 140 can include a filtermodule 121 and a filter module 131. In some scenarios, the point cloudfiltering module 140 may receive (e.g., acquire, obtain, generate or thelike) a set of POIs from a point cloud, where each POI of the set ofPOIs includes one or more points. The filter module 121 includes thefunctionality to filter a POI of a given set of POIs provided by aparticular point cloud.

As depicted in FIG. 1B, the filter module 121 may include a neighborhoodcontext module 122, a metric calculation module 123 and a decisionmodule 124. The neighborhood context module 122 includes thefunctionality to select a set of neighborhood points of a POI. Themetric calculation module 123 includes the functionality to compute oneor more metrics for a given set of neighborhood points.

The decision module 124 includes the functionality to determine, basedon a particular metric, whether to, among other things, accept the POI,modify the POI, reject the POI, or transmit the POI to the subsequentfilter 131. In some scenarios, provided a POI is accepted or modified ata particular filter module, the decision module 124 includes thefunctionality to transmit a POI to another point cloud. In somescenarios, provided a POI is rejected at a particular filter, thedecision module 124 can be configured to prevent the POI from reaching aparticular point cloud, for example, an output point cloud.

In some scenarios, provided a POI is not accepted, modified, or rejectedat a particular filter, the decision module 124 can be configured totransmit the POI to the filter module 131 to determine whether toaccept, modify, or reject the POI.

According to some embodiments, the filter module 131 includes aneighborhood context module 132, a metric unit 133 and a determinationunit 134 that are separate from the modules depicted in filter module121. For instance, the neighborhood context module 132 includes thefunctionality to select a different set of neighborhood points for aparticular POI. The metric calculation module 133 includes thefunctionality to compute a different metric for the different set ofneighborhood points. The decision module 134 includes the functionalityto determine, based on the different metric, whether to, among otherthings, accept the POI, modify the POI, reject the POI, or transmit thePOI to a different point cloud.

In some scenarios, provided a POI is accepted or modified, the decisionmodule 134 includes the functionality to transmit a POI to a particularpoint cloud. According to some embodiments, at least one of range andvelocity information is extracted at the point cloud. As will bedescribed in greater detail, filtering modules 121 and 131 can beincluded in a series cascaded filter. In some scenarios, the point cloudfiltering module 140 may include one or more series cascaded filters.

FIG. 2 is a time-frequency diagram 200 of an FMCW scanning signal 101 bthat can be used by a LiDAR system, such as system 100, to scan a targetenvironment according to some embodiments. In one example, the scanningwaveform 201, labeled as f_(FM)(t), is a sawtooth waveform (sawtooth“chirp”) with a chirp bandwidth Δfc and a chirp period Tc. The slope ofthe sawtooth is given as k=(Δfc/Tc). FIG. 2 also depicts target returnsignal 202 according to some embodiments. Target return signal 202,labeled as f_(FM)(t-Δt), is a time-delayed version of the scanningsignal 201, where Δt is the round trip time to and from a targetilluminated by scanning signal 201. The round trip time is given asΔt=2R/v, where R is the target range and v is the velocity of theoptical beam, which is the speed of light c. The target range, R, cantherefore be calculated as R=c(Δt/2). When the return signal 202 isoptically mixed with the scanning signal, a range dependent differencefrequency (“beat frequency”) Δf_(R)(t) is generated. The beat frequencyΔf_(R)(t) is linearly related to the time delay Δt by the slope of thesawtooth k. That is, Δf_(R)(t)=kΔt. Since the target range R isproportional to Δt, the target range R can be calculated asR=(c/2)(Δf_(R)(t)/k). That is, the range R is linearly related to thebeat frequency Δf_(R)(t). The beat frequency Δf_(R)(t) can be generated,for example, as an analog signal in optical receivers 104 of system 100.The beat frequency can then be digitized by an analog-to-digitalconverter (ADC), for example, in a signal conditioning unit such assignal conditioning unit 107 in LIDAR system 100. The digitized beatfrequency signal can then be digitally processed, for example, in asignal processing unit, such as signal processing unit 112 in system100. It should be noted that the target return signal 202 will, ingeneral, also include a frequency offset (Doppler shift) if the targethas a velocity relative to the LIDAR system 100. The Doppler shift canbe determined separately, and used to correct the frequency of thereturn signal, so the Doppler shift is not shown in FIG. 2 forsimplicity and ease of explanation. It should also be noted that thesampling frequency of the ADC will determine the highest beat frequencythat can be processed by the system without aliasing. In general, thehighest frequency that can be processed is one-half of the samplingfrequency (i.e., the “Nyquist limit”). In one example, and withoutlimitation, if the sampling frequency of the ADC is 1 gigahertz, thenthe highest beat frequency that can be processed without aliasing(Δf_(Rmax)) is 500 megahertz. This limit in turn determines the maximumrange of the system as R_(max)=(c/2)(Δf_(Rmax)/k) which can be adjustedby changing the chirp slope k. In one example, while the data samplesfrom the ADC may be continuous, the subsequent digital processingdescribed below may be partitioned into “time segments” that can beassociated with some periodicity in the LIDAR system 100. In oneexample, and without limitation, a time segment might correspond to apredetermined number of chirp periods T, or a number of full rotationsin azimuth by the optical scanner.

FIG. 3A is a block diagram that depicts system 300 a which includes theuse of a point cloud filter (e.g., point cloud filter 310) according toembodiments of the present disclosure. In some scenarios, a point cloud(e.g., 311) is a set of data points (or points) in the scene collectedusing one or more components of a light scanning system (e.g., LiDARsystem 100). It should be noted that the terms “data point” and “point”are interchangeably used in the present disclosure.

Each point has a set of coordinates, e.g., (X, Y, Z) and/or (Range,Azimuth, Elevation), which can be used by LiDAR system 100 to determinea point's location in the scene relative to the position of one or moresensors used by LiDAR system 100. Additional attributes such asvelocity, intensity, reflectivity, time recorded, metadata and the likemay also be calculated for a particular point. True detection (TD)points are points in the scene that represent an object or segment ofthe scene such as ground, foliage, etc. False alarm (FA) points areuntrue detection points, e.g., ghost points or noisy points, in thescene. FA points cannot be associated with any object or segment of thescene.

In some scenarios, FMCW LiDAR point clouds exhibit distinct noisepatterns which primarily arise from incorrect peak matching leading toFA points that appear in the scene even when nothing is present. Forexample, when an FMCW LiDAR system scans points corresponding to a fenceor a bush, a number of FA points, e.g., ghost points, may appear in thescene between the LiDAR system and the fence. The FA points havecharacteristic features which distinguish them from True Detection (TD)points. As described in greater detail herein, embodiments of thepresent disclosure can exploit these distinguishing features to identifythese points and regions, and then subsequently modify or remove themfrom the point cloud while leaving the TD points untouched. Theresulting point cloud (e.g., filtered point cloud 319) is a filtered outversion of the original point cloud (e.g., point cloud 311) without theFA points.

As described herein, the point cloud filtering performed by theembodiments can remove points from a point cloud which do not satisfy apredetermined threshold for a metric. For example, a filter may refer toa filtering technique or algorithm which processes a point cloud andoutputs a filtered point cloud. In some embodiments, a filter mayinclude a process in which a point cloud is processed, e.g., points notsatisfying a predetermined threshold for a metric are removed, and afiltered point cloud is outputted. The filtered point cloud produced byembodiments described herein may have some points modified and somepoints removed.

The filter processes, described herein according to embodiments, canprocess a predetermined number of points, e.g., a single point ormultiple points, at a time. A predetermined number of points that thefilter is configured to work on at a given time include POIs. Each POImay include one or more points. The POIs may be identified byembodiments based on a predetermined threshold, such as a velocitythreshold or other types of trivial identifiers. The filters describedherein can be configured to work on a POI at a time, where the POI mayinclude a single point or multiple points.

As will be explained in greater detail, upon receipt of one or morepoints from a point cloud, the filters described herein can work oneither a POI, a single point or multiple points, at a given time. Thesefilters can be configured to use points and statistics from theneighborhood of the POI may be provided to the filter to provide acontext. The filters can be configured to use contextual information tomake decisions made for a POI and to check if the characteristics of thePOI are consistent with the neighborhood points. The contextualinformation may include contextual data around the POI to aid the filterto make a decision on the POI, by checking POI's consistency with theneighborhood points. The embodiments described herein can be configuredto use different metrics to quantify these statistics/characteristics.Multiple filters may be used to identify FA points with characteristicsthat are different from the point cloud. The identified FA points arethen subsequently modified or removed by the described embodiments fromthe point cloud. The resulting point cloud is a filtered out version ofthe original point cloud without the FA points.

For instance, as depicted in the FIG. 3A embodiment, the point cloud 311may be received, for example, by the filter 310. In one scenario, thefilter 310 may include a point cloud fragmenter 312, a POI dispatcher313, a filter core 315, a POI collector 316, and/or a point cloudbuilder 317. The point cloud 311 may be fed into the point cloudfragmenter 312, which identifies regions of the point cloud 311 that thefilter 310 will be working on and creates one or more POIs that can besent to the POI dispatcher 313. Portions of the point cloud 311 that thefilter 310 does not work on are communicated or transmitted to the pointcloud builder 317. The point cloud fragmenter 312 identifies the regionsof the point cloud 311 that may have FA points, which in turn identifiesa set of POIs for the filter to work on. The point cloud fragmenter 312identifies the set of POIs in the regions that the filter 310 will beworking on based on a predetermined threshold, for example, a velocitythreshold. For example, if the filter 310 works on all points with avelocity >10 m/s, then the point cloud fragmenter 312 is configured toignore any points with a velocity <10 m/s. The ignored points are thentransmitted (e.g., communicated) to the point cloud builder 317 throughthe link 318 in between the point cloud fragmenter 312 and the pointcloud builder 317. The points that the filter 310 has not worked on maybe considered as approved by the filter 310 and are therefore present inthe output point cloud 319.

In one scenario, a size of the POI may be chosen, for example, aquantity of points in the POI. Then the regions of the point cloud 311that the filter 310 would be working on may be identified. The size ofthe POI and the region information may help the point cloud fragmenter312 to fragment the point cloud 311 into the set of POIs that the filtercore 315 can work on.

The POI dispatcher 313 receives the POIs from the point cloud fragmenter312 and sends the POIs to the filter core 315, one POI at a time, forprocessing. In some scenarios, this dispatch mechanism may beparallelized on multiple threads/graphics processing unit (GPU) cores orfield-programmable gate array (FPGA) for faster processing. The dispatchstrategy chosen by embodiments can depend on how the filter Core 315operates on the POI. In scenarios, multiple filter cores may beinitialized to process multiple threads or GPU cores. In thesescenarios, the POI dispatcher 313 can be configured to handle thiscoordination.

The filter core 315 houses one or more modules of the filter 310 whichcan each be configured to process the POIs, which will be discussedbelow. The filter core 315 may be configured to select a combination ofneighborhood context strategy, metric and decision that the filter willbe making. The combination may depend on the noise pattern that thefilter is configured to target.

In some embodiments, the filter 310 may be configured to make decisionson the POIs including approving, modifying, rejecting, or delegating(transmitting to another filter). Once a POI is processed, the filtermay determine a “filtered POI” which includes a decision made for thePOI (e.g., including, but not limited to, approved, modified, rejectedor delegated).

The POI collector 316 can be configured to collect the filtered POIsreceived from the filter core 315 and send the filtered POIs to thepoint cloud builder 317, for example, once all the POIs are processed.

The point cloud builder 317 can be configured to construct the pointcloud 319 from all the approved points in the POIs and the bypassed POIsreceived from the point cloud fragmenter 312. The filtered point cloud319 is output from the filter 310. In some scenarios, the filtered pointcloud 319 may have less number of points than the input point cloud 311,as the points rejected by the filter 310 may be removed from the inputpoint cloud 311. The point cloud builder 317 can be configured tooperate in tandem with the point cloud fragmenter 312. The point cloudbuilder 317 is configured to receive information related to points whichare not being processed by the filter core 315.

The filter 310 may also be configured to selectively operate on asmaller point group instead of waiting for the entire point cloud frameto be built to reduce overall system latency.

FIG. 3B is a block diagram illustrating an example of the filter core315 of the point cloud filter 310 according to embodiments of thepresent disclosure. A filter core may be configured to target noise withspecific characteristics. Multiple filter cores may be designed totackle different potential noise patterns in the scene. The function ofthe filter, as described herein, may be encompassed by the filter core.It should be appreciated that the terms “filter” and “filter core” maybe used interchangeably herein. In some embodiments, the filter core 315may include a neighborhood context module 322, a metric calculationmodule 323 and a decision module 324. The neighborhood context module322 is configured to select a set of neighborhood points of a POI. Themetric calculation module 323 is configured to compute a metric for theset of neighborhood points. The decision module 124 is configured todetermine, based on the metric, whether to accept the POI, modify thePOI, reject the POI, or transmit the POI to another filter.

In some implementations, the filter core 315 may be configured toprocess only one POI at a time. The neighborhood context module 322 canalso be configured to receive one or more neighbor POIs, statistics ofthe neighbor POIs, and/or the entire point cloud 311. This contextualinformation may be used to make a decision on the POI by checking if thecharacteristics are consistent with the neighborhood points. In someinstances, a metric may be used by the embodiments described herein forthe type of noise that the filter is processing. After processing thePOI, the decision module 124 may be configured to perform one or moreactions including, but not limited to, accepting, modifying, discarding,or transmitting (delegating) the POI.

FIG. 4A is a block diagram of a system 400 a that includes a simplefilter core 415 a as used in accordance with embodiments of the presentdisclosure. It should be noted that the term “filter” and “filter core”may be used interchangeably in this disclosure. According to someembodiments, a filter can operate on a set of POIs in a point cloud thatmay be based on, for example, a combination of different selection ofneighborhood data points, different metrics, and/or decisions. Thefilter may be a simple filter or a complex filter, as described herein.It should be appreciated that the examples described herein are only forillustration purposes. There may be many other embodiments of a simplefilter core based on this disclosure.

Referring to FIG. 4A, in some scenarios, a simple filter core, e.g., thesimple filter core 415 a, includes fewer computational resources toperform the filtering operations described herein relative to a complexfilter core, e.g., a complex filter core 515 a (which will be describedin FIG. 5A). For instance, the computational resources resident on thesimpler filter core, e.g., the simpler filter core 415 a, enable thesimpler filter core to perform filtering operations that require shortercompute time, access fewer resources, and/or consume less power relativeto the computational resources required by the complex filer core, e.g.,the complex filer core 515 a. For instance, predetermined thresholds canbe used to determine the amount of processing to be performed at thesimple filter or the simple filter core (e.g., including, but notlimited to, compute time thresholds, power thresholds, and the like).Additional examples that demonstrate the hardware/software profile ofsimple filters are described herein.

As depicted in FIG. 4A, the simple filter core 415 a may include aneighborhood context module 422 a, a metric calculation module 423 a,and a decision module 424 a. The neighborhood context module 422 a maybe configured to select a window of points nearby for a POI 430 a, forexample, a window of data points around the POI having either the sameazimuth/elevation.

The metric calculation module 423 a may be configured to compute aconfidence metric based on a similarity of point properties, e.g.,velocity, across the POI and the selected neighborhood data points. Thesimple filter core 415 a may be configured to check if the POI hasproperties that are not drastically different from the neighborhoodpoints. For example, if the minimum, maximum, or range of velocity ofthe POI is within a respective predetermined threshold to the minimum,maximum, or range of the neighborhood points, then the confidence metricof the POI may be determined to be high.

The decision module 424 a may be configured to determine, based on theconfidence metric, whether to accept the POI, modify the POI, reject thePOI, or delegate/transmit the POI to the complex filter core, e.g., 515a. The POI may be approved if the confidence metric of the POI isdetermined to be high. For example, when the confidence metric is withina first predetermined threshold, the POI 430 a is accepted and becomes afiltered POI 440 a. When the confidence metric is within the firstpredetermined threshold, but there are detected inconsistencies with theneighborhood context within a particular predetermined threshold,modifications may be made to the POI. For example, point properties suchas range and/or velocity may be modified. When the confidence metric ofthe POI is not consistent with the neighborhood points, e.g., notconsistent with a second predetermined or specified threshold, the POIis classified as an FA. The POI is to be discarded or removed orfiltered out.

When a decision cannot be made, but the POI was found suspicious, thenthe POI may be marked as a delegated POI 450 a and transmitted (e.g.,delegated) to a complex filter core, e.g., the complex filter core 515a. The POI may be passed to the complex filter core, e.g., the complexfilter core 515 a. The complex filter core, e.g., the complex filtercore 515 a may be configured to make a decision to accept, modify, orreject the POI. Delegating determinations in this manner can reduce theload on the subsequent complex filter core, e.g., the complex filtercore 515 a, as the subsequent complex filter core, e.g., the complexfilter core 515 a, does not operate on the entire point cloud but onlyon a subset of points (i.e., the undetermined or transmitted POIs).

FIG. 4B is a block diagram of a system 400 b that includes a simplefilter core 415 b as used in accordance with embodiments of the presentdisclosure. FIG. 4B provides an additional example that demonstrates thetypes of functions the simple filter can perform based on its respectivehardware/software profiles described herein. For instance, simple filtercan be configured to perform the operations described herein constrainedby compute time, power consumption, etc.

With reference to FIG. 4B, the simple filter core 415 b may include aneighborhood context module 422 b, a metric calculation module 423 b,and a decision module 424 b. The neighborhood context module 422 b maybe configured to bypass selecting a neighborhood for a POI 430 b. Themetric calculation module 423 b may be configured to check if the POIhas a very low velocity (lower than a first predetermined threshold,e.g., 1 m/s) or the POI points has a very high velocity (higher than asecond predetermined threshold, e.g., 100 m/s). The decision module 424b may approve the POI if the POI has a very low velocity, the POI maybecome a filtered POI 440 b. The decision module 424 b may be configuredto reject the POI if the POI points have a very high velocity. Thedecision module 424 b may be configured to delegate (transmit) any POI,e.g., with a velocity in between the first predetermined threshold andthe second predetermined threshold, then the POI may become a delegatedPOI 450 b. Because most of the scene is usually static, any noisydynamic points may be addressed by the complex filter core, e.g., 515 b.

FIG. 5A is a block diagram of a system 500 a that includes a complexfilter core 515 a as used in accordance with embodiments of the presentdisclosure. In some scenarios, the complex filter core 515 a includesmore computational resources to perform complex filtering operationsdescribed herein. The complex filter core 515 a can be configured toperform one or more complex operations in a manner that requires accessto more computational resources, more compute time, and/or more powerrelative to a simple filter described herein. For instance,predetermined thresholds can be used to determine the amount ofprocessing to be performed at the complex filter, e.g., the complexfilter core 515 a, (e.g., including, but not limited to, compute timethresholds, power thresholds, and the like). In some scenarios, certainFA data points or POI points may require a broader neighborhood contextand/or greater computational resources for detection by the complexfilter. Additional examples that demonstrate the hardware/softwareprofile of complex filters are described herein.

For instance, with reference now to FIG. 5A, in one embodiment, thecomplex filter core 515 a may include a neighborhood context module 522a, a metric calculation module 523 a, and a decision module 524 a. Theneighborhood context module 522 a may be configured to select a 3D spaceneighborhood for a delegated POI 530 a (e.g., 450 a). To get theneighborhood data points in the neighborhood, a search tree (KD Tree,OctTree, or variants) may be constructed on all the POIs.

The metric calculation module 523 a may be configured to compute avariance of point properties including velocity, intensity, or range,etc. A variance of the POI properties may be computed or calculated overthe 3-D space neighborhood. The POI properties may include the velocity,intensity, range, or even higher order moments such as skewness andkurtosis, etc., of the data point/POI. The variance of the POIproperties (e.g., the velocity or intensity) may be calculated over theneighborhood (e.g., neighborhood data points).

The decision module 524 a may be configured to determine, based on theconfidence metric, whether to accept the POI, modify the POI, reject thePOI, or delegate/transmit the POI to another filter core. The varianceof the data point/POI properties (e.g., the velocity or intensity) maybe compared against a predetermined threshold. When the POI properties(e.g., the velocity or intensity) are lower than the predeterminedthreshold, the data point/POI may be accepted or approved to become afiltered POI 540 a. The filtered POI 540 a may be added to a filteredoutput point cloud. When the POI properties (e.g., the velocity orintensity) are not lower than the predetermined threshold, the datapoint/POI may be rejected. When a decision cannot be made, then the POImay be transmitted to the subsequent filter core.

Referring to FIG. 5B, the complex filter core 515 b may include aneighborhood context module 522 b, a metric calculation module 523 b,and a decision module 524 b. The neighborhood context module 522 b mayselect a window of data points in the same azimuth or elevation around aPOI 530 b. The neighborhood context module 522 b may select a window ofpoints adjacent in the scan pattern. In some embodiments, a plurality ofneighborhood data points including a 2-D or 3-D space neighborhood maybe selected in order to consider additional ranges of data points.

The up-chirp and down-chirp frequencies from the window of pointsadjacent in the scan pattern may be stored. The metric calculationmodule 823 may compute the metric, which may be the variance of theup-chirp or the down-chirp frequencies from the window of points. Forexample, the variance of the up-chirp frequencies, or the variance ofthe down-chirp frequencies, or the difference between the variance ofthe up-chirp frequencies and the variance of the down-chirp frequenciesmay be compared against respective predetermined thresholds.

If the variance of the up-chirp frequencies, or the variance of thedown-chirp frequencies, or the difference between the variance of theup-chirp frequencies and the variance of the down-chirp frequencies isnot lower than the respective predetermined threshold, the decisionmodule 524 b may determine to reject the POI. Otherwise, the data POImay be approved to become a filtered POI 540 b or delegated/transmittedto another filter (not shown).

FIG. 6 is a block diagram of a system 600 that includes a seriescascaded point cloud filter 610 according to embodiments of the presentdisclosure. According to some embodiments, the series cascaded pointcloud filter 610 may include at least one simple filter 602 (e.g., 415a, 415 b) and at least one complex filter 603 (e.g., 515 a, 515 b). Aninput point cloud 601 may be filtered by the simple filter 602 and thecomplex filter 603. An output point cloud 604 may be the filtered pointcloud. In some scenarios, simple filter 602 includes fewer computationalresources to perform the filtering operations described herein relativeto the complex filter 603. In such cases, the computational resourcesrequired by the simpler filter 602 enable it to perform filteringoperations that require shorter compute time relative to thecomputational resources resident on the complex filter 603.

As discussed above, the simple filter 602 can be configured to performcomputationally inexpensive operations to determine neighborhood datapoints or compute the metric in a manner that requires fewercomputational resources, less compute time, and/or less power relativeto the complex filter 603.

As an example, the series cascaded point cloud filter may include thesimple filter core 415 a as described in connection with FIG. 4A and thecomplex filter core 515 a as described in connection with FIG. 5A. Asanother example, the series cascaded point cloud filter may include thesimple filter core 415 b as described in connection with FIG. 4B and thecomplex filter core 515 b as described in connection with FIG. 5B. Itshould be appreciated that the examples described herein are only forillustration purposes. There may be many other embodiments of the seriescascaded filter based on this disclosure.

Filter performance may be measured by one or more of the following:latency, computational load, FA rejected, or TD approved. An idealfilter should take less time to process the point cloud, thereby addinga lower latency. Computational load refers to an amount of computationalresources required because of the introduction of the filter. An idealfilter takes less amount of computational resources to process the pointcloud. FA rejected refers to a quantity of FA points or a number of FApoints removed by the Filter. An ideal filter removes all the FA pointsin the POIs which the filter processes. FA rejected may be determinedby:

m _(FA)=removed_(FA)/total_(FA)

TD approved refers to a quantity of TD points or a number of TD pointsapproved by the filter. An ideal filter approves all the TD points inthe POIs which the filter processes. TD approved may be determined by:

m _(TD)=approved_(TD)/total_(TD)

A combination of different filters may be employed to achieve an optimalperformance. For example, a combination of one or more simple filtersand one or more complex filters may be used to balance the accuracy, theoperational cost and the latency.

Referring to FIG. 6, embodiments of the present disclosure can beconfigured to use cascaded filters when processing point clouds in amanner that reduces latency. The series cascaded filter 610 may beconfigured using one or more functions provided by both a simple filtercore and a complex filter core. For example, simple filters may operateon as many POIs as possible, while suspect POIs may be transmitted ordelegated to complex filters to operate on. The series cascaded filter610 has the advantages of being almost as fast as a simple filter whilehaving high FA rejected MFA and high TD approved m_(TD). By using thecombination of the simple filter core and the complex filter core, theseries cascaded filter may have a high FA rejection percentage and ahigh TD approval percentage while saving the computing time and theamount of computational resources to process the point cloud, therebyimproving the performance with a lower latency.

The input point cloud 601 may be received by the series cascaded filter610. At the simple filter 602, each POI of the input point cloud 601 isfiltered. The simple filter may accept or discard as many POIs aspossible, and transmit or delegate the suspect POIs to the complexfilter 603. At the complex filter 603, the suspect POIs may be acceptedor discarded. The filtered point cloud 604 may be output based on thesimple filter 602 and the complex filter 603.

FIG. 7 is a block diagram 700 illustrating detailed structure of theseries cascaded point cloud filter 610 according to some embodiments.For example, the series cascaded point cloud filter 610 may include apoint cloud fragmenter 712, a POI dispatcher 713, a simple filter core715 (e.g., the filter core for the simple filter 602), a complex filtercore 725 (e.g., the filter core for the complex filter 603), a POIcollector 716, a POI collector and dispatcher 726, and/or a point cloudbuilder 717. The point cloud 601 may be obtained (e.g., received,acquired) by the point cloud fragmenter 712, which identifies regions ofthe point cloud 601 that the filter 610 will be working on and createsPOIs that can be sent to the POI dispatcher 713. Portions of the pointcloud 601 that the filter 610 won't be working on may be transmitted tothe point cloud builder 717 (link not shown). The POI dispatcher 713receives the POIs from the point cloud fragmenter 712 and sends the POIsto the simple filter core 715 one POI at a time for processing.

According to some embodiments, the POI collector and dispatcher 726 maybe placed in between the simple filter core 715 and the complex filtercore 725 to aid in the transfer of the POIs to the complex filter core725 for further processing to render a determination. Once the complexfilter core 725 operates on the POI, the POI is sent to the POIcollector 716, which collects the POIs processed from the simple filtercore 715 and the complex filter core 725. These pooled POIs are thensent to the point cloud builder 717.

FIG. 8 is a block diagram of a system 800 that includes multiple seriescascaded point cloud filters (e.g., 610, 810, 820) according toembodiments of the present disclosure. Using multiple series cascadedpoint cloud filters in the manner described herein enable embodiments ofthe present disclosure to achieve an optimal performance to balanceaccuracy, operational costs and latency.

Referring to FIG. 8, the series cascaded point cloud filter 610 may befollowed by a series cascaded point cloud filter 810. The output pointcloud 604 of the series cascaded point cloud filter 610 may be thefiltered point cloud 1, which may be an input point cloud for the seriescascaded point cloud filter 810. The series cascaded point cloud filter810 may include a simple filter 802 and a complex filter 803. The simplefilter 802 has similar functions like the simple filter 602. The complexfilter 803 has similar functions like the complex filter 603. Thus, theseries cascaded point cloud filter 810 may include one or more functionsof the simple filter 602 and the complex filter 603.

Similarly, the series cascaded point cloud filter 810 may be followed bya series cascaded point cloud filter 820, which may include anadditional simple filter (e.g., simple filter 812) and an additionalcomplex filter 813 (e.g., complex filter 813). The output point cloud804 of the series cascaded point cloud filter 810 may be the filteredpoint cloud 2, which may be an input point cloud for the series cascadedpoint cloud filter 820. In this fashion, embodiments of the presentdisclosure can use multiple series cascaded point cloud filters toperform filtering techniques.

FIG. 9 is a flow diagram illustrating an example of a process to filtera point cloud according to embodiments of the present disclosure. Forexample, the process may be performed by a signal processing unit 112 ofa LiDAR system, as illustrated in FIG. 1A-FIG. 1B. In this process, theFA points may be removed, thereby, the accuracy in the estimated targetrange/velocity may be improved.

At block 901, a set of POIs of a first point cloud are received, whereeach POI of the set of POIs includes one or more points. In oneembodiment, receiving the set of POIs further includes identifying theset of POIs of the first point cloud based on a predetermined threshold.

At block 902, each POI of the set of POIs is filtered, where filteringcomprises filtering at a first filter and filtering at a second filter.

At block 903, each POI of the set of POIs is filtered at the firstfilter.

At block 904, a first set of neighborhood points of a POI is selected.

At block 905, a first metric for the first set of neighborhood points iscomputed.

At block 906, based on the first metric, whether to accept the POI,modify the POI, reject the POI, or transmit the POI to a second filteris determined.

At block 907, provided the POI is accepted or modified at the firstfilter, the POI is transmitted to a second point cloud, which may be anoutput point cloud. In one embodiment, the POI is accepted or modifiedin response to the first metric satisfying a first predeterminedthreshold established for the first metric.

At block 908, provided the POI is rejected at the first filter, the POIis prevented from reaching the second point cloud. In one embodiment,the POI is rejected in response to the first metric not satisfying asecond predetermined threshold established for the first metric.

At block 909, provided the POI is not accepted, modified, or rejected atthe first filter, the POI is transmitted to a second filter to determinewhether to accept, modify, or reject the POI.

At block 910, at the second filter, provided the POI is accepted ormodified, the POI is transmitted to the second point cloud.

In one embodiment, at the second filter, a second set of neighborhoodpoints of the POI is selected. A second metric for the second set ofneighborhood points is computed. Based on the second metric, whether toaccept the POI, modify the POI, reject the POI, or transmit the POI to athird point cloud is determined.

In one embodiment, the POI is transmitted from the second point cloud,to a third point cloud, wherein the POI is accepted or modified at athird filter comprising one or more functions of the first and secondfilter.

In one embodiment, computing the second metric further includes usingmore computational resources to compute the second metric at the secondfilter relative to computing the first metric at the first filter.

In one embodiment, selecting the first set of neighborhood pointsfurther comprises using fewer computational resources to select thefirst set of neighborhood points at the first filter relative toselecting the second set of neighborhood points at the second filter.

At block 911, at least one of range and velocity information isextracted based on the second point cloud.

The preceding description sets forth numerous specific details such asexamples of specific systems, components, methods, and so forth, inorder to provide a thorough understanding of several examples in thepresent disclosure. It will be apparent to one skilled in the art,however, that at least some examples of the present disclosure may bepracticed without these specific details. In other instances, well-knowncomponents or methods are not described in detail or are presented insimple block diagram form in order to avoid unnecessarily obscuring thepresent disclosure. Thus, the specific details set forth are merelyexemplary. Particular examples may vary from these exemplary details andstill be contemplated to be within the scope of the present disclosure.

Any reference throughout this specification to “one example” or “anexample” means that a particular feature, structure, or characteristicdescribed in connection with the examples are included in at least oneexample. Therefore, the appearances of the phrase “in one example” or“in an example” in various places throughout this specification are notnecessarily all referring to the same example.

Although the operations of the methods herein are shown and described ina particular order, the order of the operations of each method may bealtered so that certain operations may be performed in an inverse orderor so that certain operations may be performed, at least in part,concurrently with other operations. Instructions or sub-operations ofdistinct operations may be performed in an intermittent or alternatingmanner.

The above description of illustrated implementations of the invention,including what is described in the Abstract, is not intended to beexhaustive or to limit the invention to the precise forms disclosed.While specific implementations of, and examples for, the invention aredescribed herein for illustrative purposes, various equivalentmodifications are possible within the scope of the invention, as thoseskilled in the relevant art will recognize. The words “example” or“exemplary” are used herein to mean serving as an example, instance, orillustration. Any aspect or design described herein as “example” or“exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Rather, use of the words“example” or “exemplary” is intended to present concepts in a concretefashion. As used in this application, the term “or” is intended to meanan inclusive “or” rather than an exclusive “or”. That is, unlessspecified otherwise, or clear from context, “X includes A or B” isintended to mean any of the natural inclusive permutations. That is, ifX includes A; X includes B; or X includes both A and B, then “X includesA or B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Furthermore, the terms “first,” “second,” “third,” “fourth,” etc.as used herein are meant as labels to distinguish among differentelements and may not necessarily have an ordinal meaning according totheir numerical designation.

What is claimed is:
 1. A method in a light detection and ranging (LiDAR)system, comprising: performing a series of cascaded filtering of a setof points of interest (POIs) in a first point cloud and a second pointcloud of the LiDAR system; calculating at least a first metric over atleast the POI and to make a decision with respect to the second pointcloud; and extracting at least one of range and velocity informationbased on the second point cloud.
 2. The method of claim 1, furthercomprising identifying the set of POIs of the first point cloud based onpoint characteristics.
 3. The method of claim 3, wherein the pointcharacteristics comprise the at least one of range and velocity.
 4. Themethod of claim 1, wherein calculating the at least first metriccomprises computing the first metric for a first set of neighborhoodpoints of the POI.
 5. The method of claim 1, further comprisingselecting the first set of neighborhood points of the POI.
 6. The methodof claim 1, wherein performing a series of cascaded filtering comprises:operating on the first POI with a first filter; transmitting the POI toa second filter; operating on the POI to a second filter; andtransmitting the POI to the second point cloud.
 7. The method of claim1, wherein performing a series of cascaded filtering comprises:operating on the first POI with a first filter; rejecting the first POIat the first filter; and preventing the POI from reaching the secondpoint cloud.
 8. A light detection and ranging (LiDAR) system,comprising: a memory; and a processing device, operatively coupled withthe memory, to: perform a series of cascaded filtering of a set ofpoints of interest (POIs) in a first point cloud and a second pointcloud of the LiDAR system; calculate at least a first metric over atleast the POI and to make a decision with respect to the second pointcloud; and extract at least one of range and velocity information basedon the second point cloud.
 9. The LiDAR system of claim 8, wherein theprocessing device is further to identify the set of POIs of the firstpoint cloud based on point characteristics.
 10. The LiDAR system ofclaim 9, wherein the point characteristics comprise the at least one ofrange and velocity.
 11. The LiDAR system of claim 8, wherein tocalculate the at least first metric, the processing device is further tocompute the first metric for a first set of neighborhood points of thePOI.
 12. The LiDAR system of claim 8, wherein the processing device isfurther to select the first set of neighborhood points of the POI. 13.The LiDAR system of claim 8, wherein to perform the series of cascadedfiltering, the processing device is to: operate on the first POI with afirst filter; transmit the POI to a second filter; operate on the POI toa second filter; and transmit the POI to the second point cloud.
 14. Anon-transitory machine-readable medium having instructions storedtherein, which when executed by a processing device of a light detectionand ranging (LiDAR) system, cause the processing device to: perform aseries of cascaded filtering of a set of points of interest (POIs) in afirst point cloud and a second point cloud of the LiDAR system;calculate at least a first metric over at least the POI and to make adecision with respect to the second point cloud; and extract at leastone of range and velocity information based on the second point cloud.15. The non-transitory machine-readable medium of claim 14, wherein theprocessing device is further to identify the set of POIs of the firstpoint cloud based on point characteristics.
 16. The non-transitorymachine-readable medium of claim 15, wherein the point characteristicscomprise the at least one of range and velocity.
 17. The non-transitorymachine-readable medium of claim 14, wherein to calculate the at leastfirst metric, the processing device is further to compute the firstmetric for a first set of neighborhood points of the POI.
 18. Thenon-transitory machine-readable medium of claim 14, wherein theprocessing device is further to select the first set of neighborhoodpoints of the POI.
 19. The non-transitory machine-readable medium ofclaim 14, wherein to perform the series of cascaded filtering, theprocessing device is to: operate on the first POI with a first filter;transmit the POI to a second filter; operate on the POI to a secondfilter; and transmit the POI to the second point cloud.
 20. Thenon-transitory machine-readable medium of claim 14, wherein to performthe series of cascaded filtering, the processing device is to: operateon the first POI with a first filter; reject the first POI at the firstfilter; and prevent the POI from reaching the second point cloud.